Machine Learning Paradigms

Machine Learning Paradigms
Publisher Springer
Release Date
Category Technology & Engineering
Total Pages 223
ISBN 9783030137434
Rating 4/5 from 21 reviews
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This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’ and learners’ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including: • Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation; • Using learning analytics to predict student performance; • Using learning analytics to create learning materials and educational courses; and • Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.

Machine Learning Paradigms
  • Author : Maria Virvou,Efthimios Alepis,George A. Tsihrintzis,Lakhmi C. Jain
  • Publisher : Springer
  • Release Date : 2019-03-16

This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’ and learners’ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various

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Machine Learning Paradigms  Theory and Application
  • Author : Aboul Ella Hassanien
  • Publisher : Springer
  • Release Date : 2018-12-08

The book focuses on machine learning. Divided into three parts, the first part discusses the feature selection problem. The second part then describes the application of machine learning in the classification problem, while the third part presents an overview of real-world applications of swarm-based optimization algorithms. The concept of machine

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Machine Learning Paradigms
  • Author : George A. Tsihrintzis,Maria Virvou,Evangelos Sakkopoulos,Lakhmi C. Jain
  • Publisher : Springer
  • Release Date : 2019-07-06

This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary; accordingly,

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AI and Machine Learning Paradigms for Health Monitoring System
  • Author : Hasmat Malik,Nuzhat Fatema,Jafar A. Alzubi
  • Publisher : Springer Nature
  • Release Date : 2021

This book embodies principles and applications of advanced soft computing approaches in engineering, healthcare and allied domains directed toward the researchers aspiring to learn and apply intelligent data analytics techniques. The first part covers AI, machine learning and data analytics tools and techniques and their applications to the class of

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Algorithms in Machine Learning Paradigms
  • Author : Jyotsna Kumar Mandal,Somnath Mukhopadhyay,Paramartha Dutta,Kousik Dasgupta
  • Publisher : Springer Nature
  • Release Date : 2020-01-03

This book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse applications, including prediction, concept learning, explanation-based learning, case-based (exemplar-based) learning, statistical rule-based learning, feature extraction-based learning, optimization-based learning, quantum-inspired learning, multi-criteria-based learning and hybrid intelligence-based learning.

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Machine Learning Paradigms
  • Author : Dionysios Sotiropoulos,George A. Tsihrintzis
  • Publisher : Springer
  • Release Date : 2016-11-24

The topic of this monograph falls within the, so-called, biologically motivated computing paradigm, in which biology provides the source of models and inspiration towards the development of computational intelligence and machine learning systems. Specifically, artificial immune systems are presented as a valid metaphor towards the creation of abstract and high

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Emerging Paradigms in Machine Learning
  • Author : Sheela Ramanna,Lakhmi C Jain,Robert J. Howlett
  • Publisher : Springer Science & Business Media
  • Release Date : 2012-07-31

This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of machine learning makes it a very fascinating and popular area for research. The book is aiming at students, practitioners and researchers

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Machine Learning Paradigms
  • Author : George A. Tsihrintzis,Dionisios N. Sotiropoulos,Lakhmi C. Jain
  • Publisher : Springer
  • Release Date : 2018-07-03

This book explores some of the emerging scientific and technological areas in which the need for data analytics arises and is likely to play a significant role in the years to come. At the dawn of the 4th Industrial Revolution, data analytics is emerging as a force that drives towards

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Lifelong Machine Learning
  • Author : Zhiyuan Chen,Bing Liu
  • Publisher : Morgan & Claypool Publishers
  • Release Date : 2018-08-14

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine

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Next Generation Wireless Networks
  • Author : Sirin Tekinay
  • Publisher : Springer Science & Business Media
  • Release Date : 2006-04-18

This book is a collection of extended versions of the papers presented at the Symposium on Next Generation Wireless Networks, May 26, 2000, New Jersey Institute of Technology, Newark, NJ. Each chapter includes, in addition to technical contributions, a tutorial of the corresponding area. It has been a privilege to bring together

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New Learning Paradigms in Soft Computing
  • Author : Lakhmi C. Jain,Janusz Kacprzyk
  • Publisher : Physica
  • Release Date : 2013-06-05

Learning is a key issue in the analysis and design of all kinds of intelligent systems. In recent time many new paradigms of automated (machine) learning have been proposed in the literature. Soft computing, that has proved to be an effective and efficient tool in so many areas of science

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Machine Learning Paradigms
  • Author : George A. Tsihrintzis,Lakhmi C. Jain
  • Publisher : Springer Nature
  • Release Date : 2020-08-24

At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some

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Understanding Machine Learning
  • Author : Shai Shalev-Shwartz,Shai Ben-David
  • Publisher : Cambridge University Press
  • Release Date : 2014-05-19

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

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Emerging Paradigms in Machine Learning
  • Author : Sheela Ramanna,Lakhmi C. Jain,Robert J. Howlett
  • Publisher : Springer
  • Release Date : 2014-08-09

This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of machine learning makes it a very fascinating and popular area for research. The book is aiming at students, practitioners and researchers

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Machine Learning Paradigms
  • Author : Aristomenis S. Lampropoulos,George A. Tsihrintzis
  • Publisher : Springer
  • Release Date : 2015-06-13

This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It

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